Can Agents-A1 35B A3B run on Mac mini M4 64GB?

YES — Runs Great

A84Great

Estimated from fit model

Agents-A1 35B A3B needs ~30.4 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 29.5 GB, 13.1 tok/s, Runs well

29.5 GB required46.1 GB available

64% VRAM used

Fit status

Runs well

Decode

13.1 tok/s

TTFT

14774 ms

Safe context

262K

Memory

29.5 GB / 46.1 GB

Memory breakdown

Weights21.4 GB

KV Cache0.3 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsAgents-A1 35B A3B on Mac mini M4 64GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 13.1 tok/s decode · 14.8s TTFT (warm) · 33 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	12.0 tok/s	8764 ms	221K
Coding	A	Runs well	12.0 tok/s	16067 ms	221K
Agentic Coding	A	Runs well	12.0 tok/s	23370 ms	221K
Reasoning	A	Runs well	12.0 tok/s	18988 ms	221K
RAG	A	Runs well	12.0 tok/s	29212 ms	221K

Inference speed

Agents-A1 35B A3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Agents-A1 35B A3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~139 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	139.1	Fits
RTX 4090 24GB	24 GB	Q4_K_M	77.7	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	76.8	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	65.5	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	64.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	62.1	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	60.7	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	47.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	47.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	36.4	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	33.4	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	31.9	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	28.3	Too big
RTX 4070 12GB	12 GB	Q4_K_M	9.9	Too big
RTX 3060 12GB	12 GB	Q4_K_M	5.8	Too big
RTX 4060 8GB	8 GB	Q4_K_M	4.4	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Agents-A1 35B A3B (35.099998474121094B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	5.1 GB	Very Low	A77
Q2_0_G128	1.71	9.4 GB	Low	A78
Q2_K	2	13.7 GB	Low	A79
Q3_K_S	3	17.2 GB	Low	A80
NVFP4	4	19.7 GB	Medium	A81
Q4_K_M	4	21.4 GB	Medium	A82
Q5_K_M	5	25.3 GB	High	A83
Q6_K	6	28.8 GB	High	A83
Q8_0Best for your GPU	8	37.6 GB	Very High	A82
F16	16	72.0 GB	Maximum	F0

Get started

Copy-paste commands to run Agents-A1 35B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "InternScience/Agents-A1" \
  --hf-file "Agents-A1-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can Mac mini M4 64GB run Agents-A1 35B A3B?

Yes, Mac mini M4 64GB can run Agents-A1 35B A3B with a A grade (Runs well). Expected decode speed: 12.0 tok/s.

How much VRAM does Agents-A1 35B A3B need?

Agents-A1 35B A3B (35.099998474121094B parameters) requires approximately 30.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Agents-A1 35B A3B?

The recommended quantization for Agents-A1 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Agents-A1 35B A3B run at on Mac mini M4 64GB?

On Mac mini M4 64GB, Agents-A1 35B A3B achieves approximately 12.0 tokens per second decode speed with a time-to-first-token of 16067ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Agents-A1 35B A3B for coding?

For coding workloads, Agents-A1 35B A3B on Mac mini M4 64GB receives a A grade with 12.0 tok/s and 221K context.

What context window can Agents-A1 35B A3B use on Mac mini M4 64GB?

On Mac mini M4 64GB, Agents-A1 35B A3B can safely use up to 221K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for Agents-A1 35B A3B?

Not always. Mac mini M4 64GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac mini M4 64GB See all hardware for Agents-A1 35B A3B

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